Single cell and spatial transcriptomic analyses reveal microglia-plasma cell crosstalk in the brain during Trypanosoma brucei infection

Human African trypanosomiasis, or sleeping sickness, is caused by the protozoan parasite Trypanosoma brucei and induces profound reactivity of glial cells and neuroinflammation when the parasites colonise the central nervous system. However, the transcriptional and functional responses of the brain to chronic T. brucei infection remain poorly understood. By integrating single cell and spatial transcriptomics of the mouse brain, we identify that glial responses triggered by infection are readily detected in the proximity to the circumventricular organs, including the lateral and 3rd ventricle. This coincides with the spatial localisation of both slender and stumpy forms of T. brucei. Furthermore, in silico predictions and functional validations led us to identify a previously unknown crosstalk between homeostatic microglia and Cd138+ plasma cells mediated by IL-10 and B cell activating factor (BAFF) signalling. This study provides important insights and resources to improve understanding of the molecular and cellular responses in the brain during infection with African trypanosomes.

-In panel 3G the authors show the result of a DEG analysis in the 6 different microglial clusters between Naive and 25/45dpi. The cell populations, naive, 25dpi, and 45dpi are very different in terms of gene detection level. Seeing this many genes differentially regulated (more than the median gene detection in the cells) indicates that the genes that appear in the DEG list are not robustly expressed across most cells. Furthermore, the comparison within the Aif1/2 types between naive and 25/45dpi is very unbalanced in terms of cells from each condition included in that clusters. The Aif1/2 types hardly contain any naive cells. Both the gene detection level and the cell numbers in each population can lead to overinterpretation of the result.
-There are some problems with the tissue used for visium analysis. The brain sections are cracked and some show significant folding. Both these issues cause an over-or underrepsentation of genes in the visium spot. The visium data was aligned to both M musculus and T brucei genomes. Pathway analysis was performed on T brucei transcript but there is no mention of how many genes per spot were detected for that genome. I would assume that gene detection levels are lower than for the mouse genome. How meaningful than is the pathway analysis just on those genes?
Minor: - Figure 3F, remove the dashed lines to figre 3E. Panel F is not taken from that area in panel E. It would be better to include a representative atlas image.
-The supplemental tables are hard to read without formatting and Table S3C is too small to read. -

Reviewer #4 (Remarks to the Author):
Previous work has demonstrated that T. brucei infection results in glial cell activation and neuroinflammation. However, the molecular mechanisms that govern these processes are unknown. This work utilizes two powerful approaches, scRNAseq and spatial transcriptomics, to define the transcriptional responses of different classes of immune cells within a well-defined area of the brain at two different time points in a rodent model of T. brucei infection.
This work is of interest to researchers from diverse fields including molecular parasitologists, immunologists, and bioinformaticians that are working to integrate multiple, distinct, large-scale 'omics data sets to understand dynamic responses. Overall, I found the study to be well designed and informative. This reviewer agrees that the work represents a useful resource for studying cellular and molecular events that occur during brain infection and provides a model for how the integration of single cell RNA seq and spatial transcriptomics can be used to study host-parasite interactions.
There were several instances where the transcripts mentioned in the text were not listed in the figure and cell clusters mislabeled. It would be important to carefully proofread all figures and text for any discrepancies. I have noted some below in the major and minor points, but they are not exhaustive and there may be additional differences I haven't detected.
Questions/comments on text: •Lines 204-209 regarding the identification of outliers. Is it possible to document genes that were flagged as "outliers". Might it be useful to other researchers to know the identity of these outliers? Also, do genes have to be identified by both approaches before being designed as outliers.? Can the authors provide a reference for "highly variable genes such as long non-coding RNAs such as Malat1?
• Table "List of RNAscope probes". To what is "channel" referring? Would it be better to indicate wavelength and/or detector?
•Lines 431-432. How do the 500 genes/cell and 1500 transcript/cell compare to other studies? Is this higher or lower than expected?
•Line 449-in reference to figure 1G-How was the inflammatory module score calculated?
•Line 607-I could not find Icam1 in the Figure 4F graph •Line 622, I could not find Sox10 and S100b in figure 1D •Lines 625-628: There percentages of each cell cluster given in the text does not agree with the image in figure 5A.
•Lines 1025, Figure 5 legend. There is no description of panel L. •There are several spatial transcriptome figures including Figure 2A, Figure 3E, and Figure 5E that are described differently. Can the authors use consistent descriptors or highlight the differences between the experiments.
Questions/comments on Figures: • Figure 1A). The right side of the figure panel is not clear. This can likely be solved by more information in the figure legend detailing the significance of the different colors.
• Figure 1C) What are the parameters used to determine clinical score and how are they quantified?
• Figure 1D) How are inflammatory gene module scores determined? I couldn't not find a reference for "in silico gene module" score • Figure 1E) I interpreted the heat map to be comparisons among the cell lines in the figure and not between infected and naïve samples. Is that correct?
• Figure 2) I found this figure particularly compelling, and some questions came to mind. Were any brain sections stained for BOTH stumpy and slender markers simultaneously? I am curious whether there might be a mixture of slender and stumpy parasites in different regions. Is there a way to compare the number of parasites present with the amount of transcript detected in the spatial transcripts? For example, is it possible to discriminate between a few, very actively transcribing parasites from more, less actively transcribing parasites? Please provide more information for the left-hand Venn diagram in E. Was the GO Term analysis done only with the 969 transcripts that were found exclusively in the CVOs or for all 1067 transcripts?
• Figure 3B: It is unclear why some of the transcripts are in bold.
• Figure 3E: Justification for using Adgre1 and Chil3 is not provided.
• Figure 4: Lines 587-591 indicates a 2-fold increase in abundance that is not apparent in the cell proportions indicated in Figure 4D. Also the numbers of each subcluster indicated in the text in lines 589-593 do not match with the cell proportions given in the table in Figure 4D. • Figure S1. More information is needed to interpret the flow cytometry panels. Are "infected" samples from 25 or 45 dpi? Macs and Micros are not defined. The live dead assay is not provided. The gating strategy in C is not clear from the figure legend. There is not enough information in the legend to interpret panel C. Tnfsf13b and Tnfrsf17 are not shown.
• Figure S2. Could the transcript names be color coded to indicate which cluster they are associated?
• Figure S3. It is unclear to me what the quality controls experiments are testing: the integrity of the brain regions during processes or the reproducibility in the number of transcriptional clusters detected? What should the reader expect to see if the data pass quality control? Also, it is unclear to me what the 18 clusters represent. Are they the same clusters as in Figures 1 and S5? Nature Communications manuscript NCOMMS-22-13214  1  2 We thank all the reviewers for their positive assessments and helpful comments. We 3 have summarised the changes made to the manuscript in response, and we believe 4 that they have significantly improved it. 5

Decision on
Major points: 6 1. We have re-annotated the myeloid subsets identified in our single cell dataset 7 to better reflect their potential function (e.g., Cd14 + monocytes, homeostatic 8 microglia, infection-associated microglia) as suggested by reviewer 2. 9 2. We have commented on the potential role of the newly identified infection-10 associated microglia subsets in the context of chronic infection and speculate 11 how these cells might be interacting with peripheral immune cells to 12 coordinate anti-parasitic responses and during brain pathology, as 13 recommended by reviewer 2. 14 3. would be helpful to possibly rename the clusters to reduce potential confusion 71 regarding the identity of the clusters. Perhaps the clusters could be called based on 72 genes like tmem119, sall1 that are unique to microglia (even if genes like tmem119 73 are downregulated with inflammation). The text states that cells in some of the 74 clusters are expressing microglia homeostatic genes but at a lower level, which is a 75 more powerful way to define these clusters versus Aif1-expression. Given the high 76 number of CD14-expressing monocytes, it is reasonable to conclude that these cells 77 may differentiate into cells that acquire markers like cx3cr1, MHC II, and Aif1 in 78 particular.

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We thank the reviewer for their insights. As this reviewer states, clusters 0 and 1 80 display high expression levels of genes used to identify "homeostatic" microglia such 81 as Tmem119, Sall1, Cx3cr1, Hexb, P2ry12, amongst others. Clusters 4 and 5 do not 82 show detectable expression levels of homeostatic microglia-related genes, but they 83 do display a robust transcriptional programme consistent with disease-associated 84 microglia (DAM) previously reported in neurodegenerative conditions. These include 85 Aif1, Cst3, Cst7, as well as genes associated with phagocytosis and lipid metabolism 86 such as Apoe, Ctsd, and Tyrobp, amongst others (reported in table S2F). Though 87 we agree that some of these genes can be shared by infiltrating myeloid cells that 88 acquire a microglia-like phenotype in the infected brain milieu, as recently reported 89 for myeloid cells during spinal cord injury in mice 1 , we cannot decipher the ontogeny 90 of these cells with the current dataset with enough certainty. We have renamed the 91 Aif1 + clusters as "Infection-associated microglia" (IAM) and the Cx3cr1 + clusters as 92 "homeostatic microglia" throughout the text to better reflect the underlying complexity 93 of these myeloid subsets. These changes are also reflected in the updated figure 3, 94 where we have also included the marker genes for each cluster in panel 3A. 95 We have changed the following text and associated figures to reflect these changes 96 as ( Figure 3C and 3D). 106 107 2) Could comment on whether there is evidence of tertiary lymphoid structures forming 108 that would further support B cells in the CNS during infection. Do they preferentially 109 form in the circumventricular organs? 110 111 This is a really interesting question. The formation of tertiary lymphoid structures in 112 the CNS was recently reported during chronic neuroinflammatory and autoimmune 113 conditions. Based on our own data, we detect genes associated with lymphoid 114 structures such as lymphotoxin b (Ltb), Cxcl13, and Tnfsf13b, among others. We are 115 currently investigating this in more detail.

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We have added a comment on the discussion to refer to this, as follow: further work is required to determine whether these structures indeed exist in the 130 chronically infected brain, and the individual contribution of the various cell types 131 identified in this study to brain pathogenesis and circadian disruptions in sleeping 132 sickness. 133 134 3) The identification of a "disease-associated" microglia population is very 135 interesting, including whether the DAM program is beneficial or detrimental. The 136 authors state that the DAM signature appears when pathology increases, but this 137 may or may not be connected to microglia. The authors should speculate on whether 138 MHC or Dectin-1 (or any others in the DAM signature) are likely to be involved in 139 tissue destruction or a brain-protective immune response. It's a timely topic and 140 worthy of discussion. 141 We neurodegenerative disorders and infections 3-5 , but whether the interactions between 163 different subsets are detrimental or beneficial to limit brain pathology remains to be 164 fully elucidated. 165 Minor comments: 166 1) CCL2 is typically associated with the recruitment of CCR2-expressing monocytes 167 and not neutrophils (typically CXCR1 and CXCR2 for PMNs).

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We thank the reviewer for this comment. We have now amended this in line 728.

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2) BAMs also express CX3CR1… What other than Arg1 makes them anti-171 inflammatory. They are typically defined by CD206 expression, but what else might 172 make them functionally anti-inflammatory? 173 Myeloid-specific cluster 3 express high levels of Mrc1, which encodes for CD206, in 174 addition to additional markers traditionally associated with anti-inflammatory 175 macrophages. Though this cell population highly expresses several putative anti-176 inflammatory macrophage markers, we did not detect a cytokine profile typically 177 associated with this function (e.g., Il10 or Il4). Rather, we detected expression of Il1b 178 (pro-inflammatory properties) and Il18bp (anti-inflammatory properties), which may 179 indicate either heterogeneous populations within this cluster, or the expression of 180 mixed cytokines. Nevertheless, we cannot resolve these nuances with the current 181 dataset, and we are now working towards better resolving these populations in vitro 182 and in vivo. We have also amended the text as follows: 183 Line 623: Cluster 3 expresses putative marker genes associated with border-184 associated macrophages such as Lyz2, Ms4a7, Ms4a6c, Tgfbi, H2-Ab1, and Lyz2 185 6,7 , as well as gene sets characteristic of anti-inflammatory responses, such as Mrc1 186 (encoding for CD206), Chil3, Arg1, and Vegfa ( Figure 3B and S2F Table), indicative 187 of an anti-inflammatory phenotype. 188 189 Reviewer #3 (Remarks to the Author): The authors have studied the transcriptional response in the brain-hypothalamus to 192 T. brucei using single cell RNA-seq and spatial transcriptomics. 193 The methods are really well described and with the data analysis scripts being made 194 available upon publication the work should be reproducible. to the figures) good enough to distinguish major cell types, but when looking at the 203 transcriptional response to a perturbation a lot of information is lost and bias is 204 introduced. Looking at the data, the quality could probably have been improved by 205 sequencing deeper. Could the authors comment? 206 We thank the reviewer for their thoughts on this aspect of the paper and we agree 207 regarding the relatively low median number of genes per cell. To address this, we 208 have described below some points which reassured us that the data generated are 209 of good quality despite the relative low gene counts per cell in the naïve controls: 210 a. During our optimisation steps, we sequenced two biological replicates in a 211 pilot 10X experiment and consistently detected low number of genes per cell 212 from hypothalamic preparations in naïve samples ( Figure 1A). Moreover, the 213 complexity score, which should be >0.8 8 , was higher in the samples included 214 in this study compared to the ones from the pilot dataset ( Figure 1B). b. In all cases, the percentage of live cells was consistently >85% as determined 224 by flow cytometry, ruling out potential issues associated with dying/dead cells.

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We have added the corresponding % of viability per sample in table S2C. 226 c. The samples presented in this manuscript were sequenced at a saturation 227 >92%, and so we did not try to increase the depth of sequencing any further 228 as we assumed an additional ~5% sequencing would not resolve this 229 apparent low gene counts drastically. We have added the corresponding % 230 sequencing saturation per sample in table S2C. 231 d. As the reviewer indicates, our bioinformatic pipeline captured the major non-232 neuronal cell populations that we would expect to see in the hypothalamus 233 based on previously reported single cell atlases 9-13 . 234 e. Given the gene discrepancies between experimental groups, we analysed the 235 integrated dataset using two independent computational approaches (Seurat 236 and STACAS) and detected the same marker genes discussed in this 237 manuscript. Notably, the number of genes per cell differs between cell types 238 under homeostatic conditions. We draw this reviewer's attention to Figure  239 S1B (upper panel), where we reported that the B cells/oligodendrocytes 240 cluster has, on average, twice as many genes per cells than ependymocytes, 241 for example, despite uniform number of UMI per cell (Figure S1B lower  242  panel). This might indicate heterogeneity in the overall gene detection level 243 across cell type within the same sample. 244 f. We consistently detected greater median gene number per cell in infected 245 samples compared to naïve samples processed in parallel.

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It is worth noting that the reported genes and UMIs per cells vary greatly across 247 reports, with some recent studies reporting "low gene counts" for glial cells in both 248 murine and human brain tissues ranging from 400-800 genes/cell and ~1,500-3,000 249 UMIs/cell 10,11,13-15, 1 . Though these are important parameters to understand the 250 underlying biology of these cells, we have found that these are inconsistently 251 reported in the currently available literature. We have included further information 252 in the revised table S2A to increase transparency in the results presented in this 253 manuscript. Cd14 + monocyte subclusters ( Figure 3G).

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Furthermore, the comparison within the Aif1/2 types between naive and 25/45dpi is 273 very unbalanced in terms of cells from each condition included in that clusters. The 274 Aif1/2 types hardly contain any naive cells. Both the gene detection level and the cell 275 numbers in each population can lead to overinterpretation of the result.

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We agree with this reviewer that the proportion of Aif1/2 types between naïve and 278 infected samples is unbalanced. To avoid overinterpretation, we have removed this 279 comparison from figure 3G. We have now included an additional figure panel ( Figure  280 3I), showing the gene pathways enriched in Aif1/2 types based on their 281 transcriptional profile (e.g., marker genes). Additionally, we have added additional 282 text to clarify that Aif1/2 clusters were not included in the pathway analysis: 283 Line Rps15), suggesting a transcriptionally active state ( Figure 3I and Table S2I).

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We have also removed the Aif1/2 clusters from the DEGs list in supplementary table  294 S2G and S2H. We have included the complete list of enriched pathways in 295 supplementary tables S2I, as well as in the table legend as follow: 296 Line 1750: S2I) List of gene pathways identified in the IAM1 and IAM2 clusters. 297 Significant pathways are considered those with a false discovery rate (FDR) < 0.05. 298 299 -There are some problems with the tissue used for visium analysis. The brain 300 sections are cracked and some show significant folding. Both these issues cause an 301 over-or underrepsentation of genes in the visium spot. 302 The overall quality, measured by transcript and gene distribution per spot in the 303 array, was consistent across samples. We have included a separate spatial feature 304 plot depicting the overall number of genes per spot in supplementary figure 3A.

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The visium data was aligned to both M musculus and T brucei genomes. Pathway 307 analysis was performed on T brucei transcript but there is no mention of how many 308 genes per spot were detected for that genome. I would assume that gene detection 309 levels are lower than for the mouse genome. How meaningful than is the pathway 310 analysis just on those genes? 311 As Regarding the pathway analysis, we included this information using the top T. brucei-332 specific marker genes identified by Seurat to explore potential signatures that define 333 the brain-dwelling parasites compared to those reported in other tissues/organs, 334 such as the bloodstream, as this remains poorly understood. Though limited, our 335 data suggest that the parasites located in the brain ventricles display signatures of 336 both slender and stumpy developmental forms. Future work is required to explore 337 this at a finer scale (e.g., using FACS to purify ventricle-enriched parasites).

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Minor: 340 - Figure 3F, remove the dashed lines to figre 3E. Panel F is not taken from that area 341 in panel E. It would be better to include a representative atlas image. 342 We have now amended the figure and as suggested have included a representative 343 atlas image depicting the area from which the images were captured from. 344 345 -The supplemental tables are hard to read without formatting and Table S3C is too 346 small to read. 347 We thank the reviewer for picking up on this. We have increased the font size in all 348 the supplementary tables, including Table S3C. This work is of interest to researchers from diverse fields including molecular 359 parasitologists, immunologists, and bioinformaticians that are working to integrate 360 multiple, distinct, large-scale 'omics data sets to understand dynamic responses. 361 Overall, I found the study to be well designed and informative. This reviewer agrees 362 that the work represents a useful resource for studying cellular and molecular events 363 that occur during brain infection and provides a model for how the integration of 364 single cell RNA seq and spatial transcriptomics can be used to study host-parasite 365 interactions.

366
We thank the reviewer for a positive assessment of the work presented here and the 367 constructive suggestions, which we have addressed in the revised manuscript as 368 well as in the sections below: 369 370 There were several instances where the transcripts mentioned in the text were not 371 listed in the figure and cell clusters mislabeled. It would be important to carefully 372 proofread all figures and text for any discrepancies. I have noted some below in the 373 major and minor points, but they are not exhaustive and there may be additional 374 differences I haven't detected.

375
Questions/comments on text: 376 377 •Lines 204-209 regarding the identification of outliers. Is it possible to document genes that were flagged as "outliers". Might it be useful to other researchers to know 379 the identity of these outliers? Also, do genes have to be identified by both 380 approaches before being designed as outliers.? Can the authors provide a reference 381 for "highly variable genes such as long non-coding RNAs such as Malat1? 382 We thank the reviewer for flagging this. The identification of highly variable genes 383 (HVGs) is a critical step for the downstream identification of discreet cell populations 384 [20][21][22] . The functions described in the methods section (e.g., using vst selection 385 method in Seurat's FindVariableFeatures function, or plotHighestExprs in Scater) 386 allowed us to identify and reduce the impact of technical outliers (e.g., lowly 387 expressed genes with high dispersion) through variance stabilisation. Additionally, 388 the tools that identify HVGs are reported to give different results 23 , therefore we 389 employed two independent methods (Seurat and Scater) for internal comparison. 390 Thus, it is not possible to compute a set number of HVGs as they are likely to vary 391 depending on the data used as input. Overall, we found that some of the HVGs 392 identified by Seurat and Scater overlap (e.g., Malat1), but we did not require for them 393 to be identified by both packages for downstream analysis. We have included the top 394 25 most variable genes identified by Scater in our dataset in figure S1C as an 395 example, and have also amended the text in the methods section to clarify this as 396 follow: 397 Line 226: To identify gene signatures that represent highly variable genes (HVGs) 398 we employed two independent approaches: i) The Seurat FindVariableFeatures 399 function with default parameters, using vst as selection method, and ii) The 400 plotHighestExprs in Scater package 21 with default parameters, which allowed us to 401 manually inspect the HVGs detected by these methods (Figure S1C). We then 402 applied the Seurat function SCTransform for data normalisation, scaling, and 403 variance stabilisation of HVGs, regressing out for percentage of mitochondrial and 404 ribosomal genes, total UMIs, genes counts, and cell cycle genes. 405 406 • Table "List of RNAscope probes". To what is "channel" referring? Would it be better 407 to indicate wavelength and/or detector? 408 The RNAscope probes are provided in different "channels" enabling multiplexing. In 409 this table we reported the channels chosen for each of the probes. However, 410 following your question, we have amended this table to include the fluorescent dye 411 used in each case (Line 448). 412 413 •Lines 431-432. How do the 500 genes/cell and 1500 transcript/cell compare to other 414 studies? Is this higher or lower than expected? 415 This is an important question, also raised by reviewer 3. Surprisingly, there is a lot of 416 variation in the number of genes and transcripts per cell detected in previous studies 417 using single cell/nuclei transcriptomics for profiling murine hypothalamus. For 418 instance, a recent report has implemented similar cut-off as the ones reported in our 419 study using the hypothalamus from aging female mice 14 , and led to the identification 420 of similar cell populations as the ones reported here. On a separate report using 421 human microglia during Alzheimer's, the authors reported a median 844 genes/cell 422 and 1,589 UMIs/cell, with some samples reporting as low as ~400 genes/cell 15 . This 423 is also the case for non-neuronal cells from the murine spinal cord, with a median of 424 ~750 genes/cell 1 . However, other reports that profile the transcriptome of neuron and 425 non-neuron cells in the hypothalamus reported a median of ~2,500 genes/cell and 426 ~6,000 UMIs/cell 10-13,25 . These discrepancies might be due to differences in 427 experimental approaches, or regions profiled within the hypothalamus (e.g., whole 428 hypothalamus, lateral or posterior hypothalamus, etc.). 429 •Line 449-in reference to figure 1G-How was the inflammatory module score 430 calculated? 431 We first mined the integrated scRNAseq object to identify pro-and anti-inflammatory 432 cytokines using the function below; We broadly called this compendium of molecules 433 "cytokine list": 434 cytokine.list <-c(grep("^Csf", rownames(data), value = T), 435 grep("^Ifn", rownames(combined_integrated), value = T), 436 grep("^Il", rownames(combined_integrated), value = T), 437 grep("^Tnfsf", rownames(combined_integrated), value = T), 438 grep("^Cxcl", rownames(combined_integrated), value = T), 439 grep("^Cccl", rownames(combined_integrated), value = T), 440 "Tslp", "Lif", "Osm", "Tnf", "Lta", "Ltb", "Cd40l", "Fasl", 441 "Cd70", "Tgfb1", "Mif", "Cx3cl1") 442 AddModuleScore(combined, features = list(as.character(cytokine.list))) 443 444 To estimate the enrichment of T. brucei transcripts in the spatial transcriptomics 445 dataset, we employed a similar approach but mining the normalised spatial 446 transcriptomics for T. brucei-specific genes. 447 The AddModuleScore function calculates the average expression levels of each 448 gene list (in this case, the genes within the cytokine.list set) on a given dataset (e.g., 449 single cell of spatial transcriptomics), subtracted by the aggregated expression of 450 control feature sets randomly selected by the function. This leads to a corrected 451 expression level for genes of interest, in this case, (pro and anti) inflammatory 452 cytokines.

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We •Line 532-should Figure 3F be Figure 3E? 475 We thank the reviewer for spotting this. We have now amended this in 626. 476 477 •Line 607-I could not find Icam1 in the Figure 4F graph 478 We draw the reviewer's attention to the microglia cluster (dark blue; bottom right) 479 where Icam1 is depicted 480 481 •Line 622, I could not find Sox10 and S100b in figure 1D  482 We thank the reviewer for noticing this oversight and have now added these two 483 marker genes to the heatmap in figure 1D. We have also referenced table S2J that  484 should contain all the markers for these clusters as follow: 485 Line 933: This appeared to represent a heterogeneous grouping of cells expressing 486 high levels of oligodendrocyte markers (Olig1, Sox10, and S100b) and bona fide B 487 cell markers (Cd79a, Cd79b, Ighm) ( Figure 1D, S2B and S2J Table). 488 489 •Lines 625-628: There percentages of each cell cluster given in the text does not 490 agree with the image in figure 5A. 491 We thank the reviewer for noticing this error. We have now corrected this issue in 492 figure 5A. 493 494 •Lines 1025, Figure 5 legend. There is no description of panel L.

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We thank the reviewer for noticing this oversight. We have now added the following 496 text in the figure caption describing the results presented in panel L: 497 Line 1448: L) qRT-PCR analysis of pro-inflammatory mediators (Il1β and Tnfα) in 498 BV2 microglia cell lines exposed to LPS in the presence of the B cell supernatant 499 with our without an anti-IL-10 blocking antibody. Pairwise comparisons were 500 conducted against cells exposed to LPS alone using Mann-Whitney test. A p values 501 <0.05 were considered statistically significant. * p < 0.05; ** p < 0.005; *** p < 502 0.0005. 503 504 •There are several spatial transcriptome figures including Figure 2A, Figure 3E, and 505 Figure 5E that are described differently. Can the authors use consistent descriptors 506 or highlight the differences between the experiments. 507 As requested, we have now amended the captions in Figure 2A (line 1303) and 508 Figure 5G (line 1435). Please note that the panels in figure 5 have changed, but that 509 I have updated the one mentioned here.

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Questions/comments on Figures: 512 513 • Figure 1A). The right side of the figure panel is not clear. This can likely be solved 514 by more information in the figure legend detailing the significance of the different 515 colors. 516 Thank you for this suggestion. We have amended figure 1A to reflect these proposed 517 changes. 518 519 • Figure 1C) What are the parameters used to determine clinical score and how are 520 they quantified? 521 The clinical score is assessed in accordance with our Home Office animal project 522 license (No. PC8C3B25C). We have included the following statement in the Method 523 section to clarify the clinical scoring system: 524 immediately in accordance with ethical regulations in our animal project license. 530 531 • Figure 1D) How are inflammatory gene module scores determined? I couldn't not 532 find a reference for "in silico gene module" score 533 Thank you for this question. We have now addressed this as outlined above (line 534 309). 535 536 • Figure 1E) I interpreted the heat map to be comparisons among the cell lines in the 537 figure and not between infected and naïve samples. Is that correct? 538 Yes, this is correct. The heatmap shows the expression level of the top 25 marker 539 genes for each of the cell clusters identified in the hypothalamus. The heatmap is 540 organised based on abundance of each of the clusters. For instance, cluster 541 "Microglia 1" (far left) has comparatively more cells than the "Ependymocytes" cluster 542 (far right). We have clarified this in the figure legend as follow: 543 Line 1269: E) Heatmap representing the expression level of the top 25 marker 544 genes for each of the cell clusters identified in the combined dataset. 545 546 • Figure 2) I found this figure particularly compelling, and some questions came to 547 mind. Were any brain sections stained for BOTH stumpy and slender markers 548 simultaneously? I am curious whether there might be a mixture of slender and 549 stumpy parasites in different regions. 550 We thank the reviewer for this question. Indeed, we stained the samples presented 551 in figure 2D with both stumpy and slender markers but did not include the composite 552 image in the original submission for simplicity. However, we have now included the 553 composite in this figure and have amended the legend accordingly. Though we 554 cannot confidently assign spatial segregation of the different forms, we do tend to 555 see slender forms more scattered across the ventricles and surrounding tissue, 556 whereas the stumpy forms are mostly confined to the choroid plexus. 557 558 Is there a way to compare the number of parasites present with the amount of 559 transcript detected in the spatial transcripts? For example, is it possible to 560 discriminate between a few, very actively transcribing parasites from more, less 561 actively transcribing parasites? 562 This is a great question. Unfortunately, we cannot address this question as the 10X 563 Visium data is not resolved at a single cell level. Rather, we are only able to resolve 564 transcriptional spots, in which there might be an enrichment of "slender" transcripts, 565 "stumpy" transcripts, or a mixture of both. Future experiments sorting parasites from 566 various brain regions and comparing them using single cell transcriptomics would 567 allow us to resolve these populations in greater detail. 568 569 Please provide more information for the left-hand Venn diagram in E. Was the GO 570 Term analysis done only with the 969 transcripts that were found exclusively in the 571 CVOs or for all 1067 transcripts? 572 We have amended the figure legend to provide more details. For simplicity, the GO 573 term analysis was conducted on the 969 (~90%) transcripts detected in the CVOs. 574 We draw the reviewer's attention to line 1287: E) Ven diagram of the different T. 575 brucei-specific transcripts detected in several brain regions at 45dpi, based on the 576 spatial distribution shown in 4B. Top 10 GO terms that characterise brain-dwelling 577 African trypanosomes located in the CVOs. 578 579 • Figure 3: There is a discrepancy regarding significance levels. Figure legend (lines 580 947-948) indicates "-2 < Log2Fold change < 2 adjusted p value < 0.05" while the text 581 (lines 544-545) indicates "Log2fold change > 0.25 or < -0. 25". 582 We thank the reviewer for noticing this error. We have amended this in line 1324 as 583 follow: These genes were defined as having a -0.25 < Log2 Fold change < 0.25, and 584 an adjusted p value of < 0.05 585 586 • Figure 3B: It is unclear why some of the transcripts are in bold.

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We thank the reviewer for noticing this and we have amended this in the figure 588 589 • Figure 3E: Justification for using Adgre1 and Chil3 is not provided. 590 We thank the reviewer for this comment. We draw their attention to the text in line 591 777: Similarly, Arg1 and Chil3, putative marker genes for Mrc1 + BAMs, were 592 predominantly located in the lateral ventricle and the dorsal 3 rd ventricle in the 593 infected brain ( Figure 3E), further corroborated by immunofluorescence analysis on 594 independent brain sections ( Figure 3F). 595 596 • Figure 4: Lines 587-591 indicates a 2-fold increase in abundance that is not 597 apparent in the cell proportions indicated in Figure 4D. Also the numbers of each 598 subcluster indicated in the text in lines 589-593 do not match with the cell 599 proportions given in the table in Figure 4D. 600 We thank the reviewer for noticing this. The bar plot in figure 4D represents the three 601 main clusters (1-3) after cluster 0 was removed, as indicated in line 840. We have 602 now amended the text with the correct cell frequencies as follow: 603 Line 856: These populations seemed dynamic over the course of infection, with 604 chronic stages associated with a 1.27-and 1.61-fold increase in the abundance of 605 Cd4 + T cells compared to other subclusters (23.64%, 30.21%, and 38.1% in naïve, 606 25dpi, and 45dpi, respectively) ( Figure 4B and 4D), consistent with previous reports 607 26,27 . Of note, the subcluster identified as cluster 2 Cd8 + T cells (Cd8 + 2 T cells) was 608 only detected in infected samples but not in naïve controls (35.98% and 36.5% at 25 609 and 45dpi, respectively) ( Figure 4B), indicating a disease-associated T cell subset in 610 the brain 611 612 • Figure 5 A. Should Cluster 5 be Cluster 0? 613 Thank you for highlighting this. We have now amended cluster 5 and labelled it as 614 cluster 0 in the figure.

616
The percentages given in the text for the clusters in A do not match what is provided 617 in the figure panel 5A. For example, Cluster 1 = 34% (line 625) and Cluster 3 = 618 8.48% (lines 626). In the figure, there are fewer dots in cluster 1 than 3. There is no 619 description for Panel L. 620 In line with this comment, we have amended the labelling of the different clusters to 621 represent the corresponding percentages reported in the main text. We have also 622 added the following text to the figure caption describing the results presented in 623 panel L: 624